Isbn: 9781032978499 - advanced statistical analytics for health data science with sas and r (21 risultati)

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: New.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Condizione: New. In.

Condizione: New.

Hardcover. Condizione: Brand New. 252 pages. 9.18x6.12x9.45 inches. In Stock.

Hardcover. Condizione: new. Hardcover. In recent years, there has been a growing emphasis on making statistical methods and analytics accessible to health data science researchers and students. Following the first book on "Statistical Analytics for Health Data Science with SAS and R" (2023, , this book serves as a comprehensive reference for health data scientists, bridging fundamental statistical principles with advanced analytical techniques. By providing clear explanations of statistical theory and its application to real- world health data, we aim to equip researchers with the necessary tools to navigate the evolving landscape of health data science.Designed for advanced-level data scientists, this book covers a wide range of statistical methodologies, including models for longitudinal data with time-dependent covariates, multi-membership mixed-effects models, statistical modeling of survival data, Bayesian statistics, joint modeling of longitudinal and survival data, nonlinear regression, statistical meta-analysis, spatial statistics, structural equation modeling, latent growth curve modeling, causal inference, and propensity score analysis.A key feature of this book is its emphasis on real-world applications. We integrate publicly available health datasets and provide case studies from a variety of health applications. These practical examples demonstrate how statistical methods can be applied to solve critical problems in health science.To support hands-on learning, we offer implementation guidance using SAS and R, ensuring that readers can replicate analyses and apply statistical techniques to their own research. Step-by-step computational examples facilitate reproducibility and deeper exploration of statistical models. By combining theoretical foundations with practical applications, this book empowers health data scientists to develop robust statistical solutions for complex health challenges. Whether working in academia, industry, or public health, readers will gain the expertise to advance data-driven decision-making and contribute to evidence-based health research. Serves as a comprehensive reference for health data scientists, bridging fundamental statistical principles with advanced analytical techniques. Provides clear explanations of statistical theory and its application to real-world health data. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 264 pp. Englisch.

Hardcover. Condizione: new. Hardcover. In recent years, there has been a growing emphasis on making statistical methods and analytics accessible to health data science researchers and students. Following the first book on "Statistical Analytics for Health Data Science with SAS and R" (2023, , this book serves as a comprehensive reference for health data scientists, bridging fundamental statistical principles with advanced analytical techniques. By providing clear explanations of statistical theory and its application to real- world health data, we aim to equip researchers with the necessary tools to navigate the evolving landscape of health data science.Designed for advanced-level data scientists, this book covers a wide range of statistical methodologies, including models for longitudinal data with time-dependent covariates, multi-membership mixed-effects models, statistical modeling of survival data, Bayesian statistics, joint modeling of longitudinal and survival data, nonlinear regression, statistical meta-analysis, spatial statistics, structural equation modeling, latent growth curve modeling, causal inference, and propensity score analysis.A key feature of this book is its emphasis on real-world applications. We integrate publicly available health datasets and provide case studies from a variety of health applications. These practical examples demonstrate how statistical methods can be applied to solve critical problems in health science.To support hands-on learning, we offer implementation guidance using SAS and R, ensuring that readers can replicate analyses and apply statistical techniques to their own research. Step-by-step computational examples facilitate reproducibility and deeper exploration of statistical models. By combining theoretical foundations with practical applications, this book empowers health data scientists to develop robust statistical solutions for complex health challenges. Whether working in academia, industry, or public health, readers will gain the expertise to advance data-driven decision-making and contribute to evidence-based health research. Serves as a comprehensive reference for health data scientists, bridging fundamental statistical principles with advanced analytical techniques. Provides clear explanations of statistical theory and its application to real-world health data. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Hardback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 526.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dr Ding-Geng Chen is an elected fellow of the American Statistical Association and an elected member of the International Statistical Institute. Currently he is the executive director and professor in biostatistics at the College of Health Solutio.…

Hardcover. Condizione: new. Hardcover. In recent years, there has been a growing emphasis on making statistical methods and analytics accessible to health data science researchers and students. Following the first book on "Statistical Analytics for Health Data Science with SAS and R" (2023, , this book serves as a comprehensive reference for health data scientists, bridging fundamental statistical principles with advanced analytical techniques. By providing clear explanations of statistical theory and its application to real- world health data, we aim to equip researchers with the necessary tools to navigate the evolving landscape of health data science.Designed for advanced-level data scientists, this book covers a wide range of statistical methodologies, including models for longitudinal data with time-dependent covariates, multi-membership mixed-effects models, statistical modeling of survival data, Bayesian statistics, joint modeling of longitudinal and survival data, nonlinear regression, statistical meta-analysis, spatial statistics, structural equation modeling, latent growth curve modeling, causal inference, and propensity score analysis.A key feature of this book is its emphasis on real-world applications. We integrate publicly available health datasets and provide case studies from a variety of health applications. These practical examples demonstrate how statistical methods can be applied to solve critical problems in health science.To support hands-on learning, we offer implementation guidance using SAS and R, ensuring that readers can replicate analyses and apply statistical techniques to their own research. Step-by-step computational examples facilitate reproducibility and deeper exploration of statistical models. By combining theoretical foundations with practical applications, this book empowers health data scientists to develop robust statistical solutions for complex health challenges. Whether working in academia, industry, or public health, readers will gain the expertise to advance data-driven decision-making and contribute to evidence-based health research. Serves as a comprehensive reference for health data scientists, bridging fundamental statistical principles with advanced analytical techniques. Provides clear explanations of statistical theory and its application to real-world health data. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Buch. Condizione: Neu. Advanced Statistical Analytics for Health Data Science with SAS and R | Ding-Geng Chen (u. a.) | Buch | Englisch | 2025 | Chapman and Hall/CRC | EAN 9781032978499 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu Print on Demand. …

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering.